---
layout: post
title: "Data wrangling operations with quantities"
date: "27 June, 2018"
comments: true
author: IĆ±aki Ucar
categories: r
---
TOC
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This is the third blog post on [`quantities`](https://github.com/r-quantities/quantities), an R-Consortium funded project for quantity calculus with R. It is aimed at providing integration of the 'units' and 'errors' packages for a complete quantity calculus system for R vectors, matrices and arrays, with automatic propagation, conversion, derivation and simplification of magnitudes and uncertainties. This article investigates the compatibility of common data wrangling operations with quantities. In previous articles, we discussed [a first working prototype](https://www.r-spatial.org/r/2018/03/01/quantities-first-prototype.html) and [units and errors parsing](https://www.r-spatial.org/r/2018/05/07/parsing-quantities.html).
## Compatibility with different workflows
The bulk of this work can be found in a new vignette entitled [*A Guide to Working with Quantities*](https://github.com/r-quantities/quantities/blob/master/vignettes/introduction.Rmd). There, you may find a comprehensive set of examples of the main data wrangling operations (subsetting, ordering, transformations, aggregations, joining and pivoting) in two distincts worflows: R base and the [*tidyverse*](https://www.tidyverse.org/). Here, we intend to provide a brief summary.
As we have discussed in previous articles, quantities are implemented as S3 objects with custom units and errors attributes. All the main operators that can be applied to vectors and arrays are properly defined so that they are forwarded to the attributes. This is important to preserve units (one unit for the entire vector/array), but is critical to correctly propagate errors (one error per vector/array element). If operations are not forwarded, object corruption occurs.
### R base
Data wrangling operations on data frames map to R functions as follows:
- Row subsetting: `[` or `subset`.
- Row ordering: `[` with `order`.
- Column transformation: `within` or `transform`.
- Row aggregation: `aggregate`.
- Column joining: `merge`.
- (Un)pivoting: `reshape`.
R base functions make intensive use of the `[` generic. Therefore, as expected, all the operations work correctly with units and errors metadata. The only drawback is that aggregations by default will drop quantities metadata. The reason is that there is a family of functions (not only `aggregate`, but also `by` and the `apply` family) which holds intermediate results in lists, and these are finally simplified by calling `unlist`.
There is no workaround for this default behaviour, because it is not possible to define methods for *lists of something*. Fortunately, all these functions support a parameter called `simplify` (sometimes, `SIMPLIFY`) which, if set to `FALSE`, avoids the `unlist` call and returns the results in a list. Then, a call to `do.call(c, ...)` will unlist quantities without losing attributes or classes.
### Tidyverse
Data wrangling operations on data frames map to tidyverse functions as follows:
- Row subsetting: `dplyr::filter` (and others).
- Row ordering: `dplyr::arrange`.
- Column transformation: `dplyr::transmute` and `dplyr::mutate`.
- Row aggregation: `dplyr::summarise` (and others) with `dplyr::group_by` for observation grouping.
- Column joining: `dplyr::*_join` family.
- (Un)pivoting: `tidyr::gather` and `tidyr::spread`.
The tidyverse handles quantities correctly for subsetting, ordering and transformations. It fails to do so for aggregations (grouped operations in general), column joining and (un)pivoting. Most of these incompatibilities are due to the same internal grouping mechanism, which is in C and prevents the R subsetting operator from being called (which in turn calls the subsetting operator on the errors attribute). Interestingly, those operations still work for units alone, except for column gathering, which drops all classes and attributes. It seems though that there are long-term plans in `dplyr` for supporting vectorised attributes (see [tidyverse/dplyr#2773](https://github.com/tidyverse/dplyr/issues/2773) and [tidyverse/dplyr#3691](https://github.com/tidyverse/dplyr/issues/3691)).
### A note on `data.table`
*Currently* (v1.11.4) `data.table` does not work well with vectorised attributes. The underlying problem is similar to `dplyr`'s issue, but unfortunately it affects more operations, including row subsetting and ordering. Only column transformation seems to work, and other operations generate corrupted objects. This issue was reported on GitHub (see [Rdatatable/data.table#2948](https://github.com/Rdatatable/data.table/issues/2948)).
## Future directions of units and errors
A couple of weeks ago, I had the pleasure of visiting Edzer Pebesma at the Institute for Geoinformatics in Muenster, and we had a nice R-quantities summit.
We had a very productive discussion on the [future directions](https://github.com/r-quantities/proposal/blob/master/directions.md) of the `units` and `errors` packages. These are some of the ideas on the table:
- As a follow-up to the [previous milestone](https://www.r-spatial.org/r/2018/05/07/parsing-quantities.html), we found interesting the idea of enhancing the `readr` package to allow third-party packages to provide new column types and parsers that would work transparently. There are other interesting use cases, such as reading spatial data. We registered [the proposal](https://github.com/tidyverse/readr/issues/865) in the `readr`'s repository.
- We discussed [a recent proposal](https://github.com/r-quantities/units/issues/145) by Bill Denney (and had a most interesting chat with him) in which he requests support for *mixed units* in R vectors and arrays. Bill works with data from clinical studies and deals with a very specific format. I refer to the issue at hand (previous link and references therein) for specific examples and further discussion. Edzer already started to work on this, and there is a functional prototype in the `mixed` branch on Github.
- As a mid-term plan, we would also like to add support for other propagation methods to the `errors` package. More specifically, instead of storing a single value and an associated error (and applying TSM), we plan to provide support for full samples. Operations would work directly on these samples, so that every kind of correlation would be captured.
## Next steps
The R-quantities project is coming to an end. The next and final milestone will try to provide a proof-of-concept to wrap `lm` methods, where errors are used to define weights in the linear model and units propagate to the regression coefficient estimates and residuals. We will also complete the documentation with the prospect of a first release of the `quantities` package on CRAN.